In this paper, we show that ma-QAOA is equivalent to a restriction of dynamic CTQWs in which the underlying graphs can only consist of singletons or of discrete hypercubes with dimension at most \(n\)  and then find the appropriate operators and angles that yield the universal gate set \(H\) , \(T\) , and \(CX\)  in the ma-QAOA framework. Since ma-QAOA can be viewed as a restriction of dynamic CTQWs, there is potential that well-studied CTQW phenomena, such as hitting times, can be used to determine the optimal \(\beta \)  parameters for ma-QAOA. Finding optimal QAOA parameters is a challenging problem, and several techniques such as transferability, reinforcement learning, and neural networks [1]}, [2]}, [3]}, [4]}. Relating ma-QAOA to CTQWs gives a new framework through which to view the algorithm. It would be of interest to determine if viewing CTQWs through a QAOA lens leads to better understanding of CTQWs.
